In this paper, an efficient Computer Tomography (CT) image classification using Support Vector Machine (SVM) with optimized quadratic programming methodology is proposed. Due to manual interpretation of brain images based on visual examination by radiologist/physician that cause incorrect diagnosis, when a large number of CT images are analyzed. To avoid the human error, an automated optimized ...

Multiclass One-versus-One (OvO) SVM, which is constructed by assembling a group of binary classifiers, is usually treated as a black-box. The usual Multiclass Feature Selection (MFS) algorithm chooses an identical subset of features for every OvO SVM. We question whether the standard process of applying feature selection and then constructing the multiclass classifier is best. We propose that I...

In this paper, we study the human action classification problem based on motion features directly extracted from video. In order to implement a fast classification system, we select simple features that can be obtained from non-intensive computation. We also introduce the new SVM 2K classifier that can achieve improved performance over a standard SVM by combining two types of motion feature vec...

A new ensemble of support vector machines (SVM) based on random subspace (RS) and feature selection is developed and applied to the problem of differential diagnosis of erythemato-squamous diseases. Each classifier has a ‘‘favourite’’ class. To find the feature subset for the classifier Di with ‘‘favourite’’ class wi, we calculate the best features to discriminate this class (wi) from all the o...

Support Vector Machine (SVM) is a state-of-art machine learning algorithm broadly used classification and learning tasks. It has been recently used on Brain-Computer Interface (BCI) systems to discrimination between motor tasks of electroencephalography (EEG) signals. However, there are different possible strategies to implement the SVM classifier. In this paper1 it is compared two SVM strategi...

ABSTRACT This paper describes our work in NTCIR-9 on RITE Binary-class (BC) subtask and Multi-class (MC) subtask in Simplified Chinese. We use classification method and SVM classifier to identify the textual entailment. We totally use thirteen statistical features as the classification features in our system. The system includes three parts: (1) Preprocessing, (2) Feature Extraction, (3) SVM Cl...

Text classification is a supervised learning technique that uses labeled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. In this paper, we investigate Naïve Bayesian method (NB) and Support Vector Machine algorithm (SVM) on different Arabic data sets. The bases of our comparison are the most popula...

A new sliding window scheme is introduced with multiple windows to form the protein data for SVM. Two new tertiary classifiers are introduced; one of them makes use of support vector machines as neurons in neural network architecture and the other tertiary classifier is a granular decision tree based on granular computing, decision tree and SVM. Binary classifier using multiple windows is compa...

Classification of microarray data requires the selection of subsets of relevant genes in order to achieve good classification performance. This article presents a genetic embedded approach that performs the selection task for a SVM classifier. The main feature of the proposed approach concerns the highly specialized crossover and mutation operators that take into account gene ranking informatio...

Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for compa...